Optimization–Simulation Model for Planning Supply Transport to Large Infrastructure Public Works Located in Congested Urban Areas

This article proposes an optimization–simulation model for planning the transport of supplies to large public infrastructure works located in congested urban areas. The purpose is to minimize their impact on the environment and on private transportation users on the local road network. To achieve this goal, the authors propose and solve an optimization problem for minimizing the total system cost made up of operating costs for various alternatives for taking supplies to the worksite and the costs supported by private vehicle users as a result of increased congestion due to the movement of heavy goods vehicles transporting material to the worksite. The proposed optimization problem is a bi-level Math Program model. The upper level defines the total cost of the system, which is minimized taking into account environmental constraints on atmospheric and noise pollution. The lower level defines the optimization problem representing the private transportation user behavior, assuming they choose the route that minimizes their total individual journey costs. Given the special characteristics of the problem, a heuristic algorithm is proposed for finding optimum solutions. Both the model developed and the specific solution algorithm are applied to the real case of building a new port at Laredo (Northern Spain). A series of interesting conclusions are obtained from the corresponding sensitivity analysis.     

Convertibility of Function Points into COSMIC Function Points: A study using Piecewise Linear Regression

Background

COSMIC Function Points and traditional Function Points (i.e., IFPUG Function Points and more recent variation of Function Points, such as NESMA and FISMA) are probably the best known and most widely used Functional Size Measurement methods. The relationship between the two kinds of Function Points still needs to be investigated. If traditional Function Points could be accurately converted into COSMIC Function Points and vice versa, then, by measuring one kind of Function Points, one would be able to obtain the other kind of Function Points, and one might measure one or the other kind interchangeably. Several studies have been performed to evaluate whether a correlation or a conversion function between the two measures exists. Specifically, it has been suggested that the relationship between traditional Function Points and COSMIC Function Points may not be linear, i.e., the value of COSMIC Function Points seems to increase more than proportionally to an increase of traditional Function Points.

Objective

This paper aims at verifying this hypothesis using available datasets that collect both FP and CFP size measures.

Method

Rigorous statistical analysis techniques are used, specifically Piecewise Linear Regression, whose applicability conditions are systematically checked. The Piecewise Linear Regression curve is a series of interconnected segments. In this paper, we focused on Piecewise Linear Regression curves composed of two segments. We also used Linear and Parabolic Regressions, to check if and to what extent Piecewise Linear Regression may provide an advantage over other regression techniques. We used two categories of regression techniques: Ordinary Least Squares regression is based on the usual minimization of the sum of squares of the residuals, or, equivalently, on the minimization of the average squared residual; Least Median of Squares regression is a robust regression technique that is based on the minimization of the median squared residual. Using a robust regression technique helps filter out the excessive influence of outliers.

Results

It appears that the analysis of the relationship between traditional Function Points and COSMIC Function Points based on the aforementioned data analysis techniques yields valid significant models. However, different results for the various available datasets are achieved. In practice, we obtained statistically valid linear, piecewise linear, and non-linear conversion formulas for several datasets. In general, none of these is better than the others in a statistically significant manner.

Conclusions

Practitioners interested in the conversion of FP measures into CFP (or vice versa) cannot just pick a conversion model and be sure that it will yield the best results. All the regression models we tested provide good results with some datasets. In practice, all the models described in the paper - in particular, both linear and non-linear ones - should be evaluated in order to identify the ones that are best suited for the specific dataset at hand.     

Pairwise Registration by Local Orientation Cues

Inspired by recent work on robust and fast computation of 3D Local Reference Frames (LRFs), we propose a novel pipeline for coarse registration of 3D point clouds. Key to the method are: (i) the observation that any two corresponding points endowed with an LRF provide a hypothesis on the rigid motion between two views, (ii) the intuition that feature points can be matched based solely on cues directly derived from the computation of the LRF, (iii) a feature detection approach relying on a saliency criterion which captures the ability to establish an LRF repeatably. Unlike related work in literature, we also propose a comprehensive experimental evaluation based on diverse kinds of data (such as those acquired by laser scanners, Kinect and stereo cameras) as well as on quantitative comparison with respect to other methods. We also address the issue of setting the many parameters that characterize coarse registration pipelines fairly and realistically. The experimental evaluation vouches that our method can handle effectively data acquired by different sensors and is remarkably fast.     

Feedforward networks in financial predictions: the future that modifies the present

The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test.     

Estimation of predictive accuracy in survival analysis using R and S-PLUS

When the purpose of a survival regression model is to predict future outcomes, the predictive accuracy of the model needs to be evaluated before practical application. Various measures of predictive accuracy have been proposed for survival data, none of which has been adopted as a standard, and their inclusion in statistical software is disregarded. We developed the surev library for R and S-PLUS, which includes functions for evaluating the predictive accuracy measures proposed by Schemper and Henderson. The library evaluates the predictive accuracy of parametric regression models and of Cox models. The predictive accuracy of the Cox model can be obtained also when time-dependent covariates are included because of non-proportional hazards or when using Bayesian model averaging. The use of the library is illustrated with examples based on a real data set.     

An Integrated Assessment of the Impacts of Changing Climate Variability on Agricultural Productivity and Profitability in an Irrigated Mediterranean Catchment

Climate change is likely to have a profound effect on many agricultural variables, although the extent of its influence will vary over the course of the annual farm management cycle. Consequently, the effect of different and interconnected physical, technical and economic factors must be modeled in order to estimate the effects of climate change on agricultural productivity. Such modeling commonly makes use of indicators that summarize the among environmental factors that are considered when farmers plan their activities. This study uses net evapotranspiration (ET_N), estimated using EPIC, as a proxy index for the physical factors considered by farmers when managing irrigation. Recent trends suggest that the probability distribution function of ET_N may continue to change in the near future due to changes in the irrigation needs of crops. Also, water availability may continue to vary due to changes in the rainfall regime. The impacts of the uncertainties related to these changes on costs are evaluated using a Discrete Stochastic Programming model representing an irrigable Mediterranean area where limited water is supplied from a reservoir. In this context, adaptation to climate change can be best supported by improvements to the collective irrigation systems, rather than by measures aimed at individual farms such as those contained within the rural development policy.     

Geometry and Stacking Sequence Effect on Composite Spinnaker Pole's Stiffness: Experimental and Numerical Analysis

Composite materials are widely employed in sailing sports, a possible application is for the mast pole or other sail poles. In the paper the attention is focused on the spinnaker poles mechanical performances; in particular the focus is on axial and ring compressive properties of three different carbon fibre/epoxy resin spinnaker poles, to investigate both the diameter and stacking sequence effect on the mechanical performance of the structure. Starting from the stacking sequence used in the production of a particular spinnaker pole, the effect of a lamina at 0° in the middle of wall thickness is investigated with the purpose to obtain a more stiff structure. Moreover to test the proposed stacking sequence on different size products, a prototype with lower diameter is realized. To properly evaluate axial and ring stiffness, axial compression test and ring stiffness one are performed. Then a numerical model is developed to support the design of the finished product: A simple and versatile numerical analysis (FEA with software ANSYS), by simulating ring stiffness and pull-direction compression tests, is carried out in elastic regime. Such model should be suitable for designing and/or verifying the mechanical performance of pole structures, even though differing from those above described, for materials, geometry and stacking sequence.     

Virtual Organizations in Arigatoni

Arigatoni is a lightweight overlay network that deploys the Global Computing Paradigm over the Internet. Communication for over the behavioral units of the overlay is performed by a simple resource discovery protocol (RDP). Basic Global Computers Units (GC) can communicate by first registering to a brokering service and then by mutually asking and offering services.Colonies and communities are the main entities in the model. A colony is a simple virtual organization composed by exactly one leader and some set (possibly empty) of individuals. A community is a raw set of colonies and global computers (think it as a soup of colonies and global computer without a leader).We present an operational semantics via a labeled transition system, that describes the main operations necessary in the Arigatoni model to perform leader negotiation, joining/leaving a colony, linking two colonies and moving one GC from one colony to another. Our formalization results to be adequate w.r.t. the algorithm performing peer logging/delogging and colony aggregation.     

Impact of Freeze-Drying on Cord Blood (CB), Serum (S), and Platelet-Rich Plasma (CB-PRP) Preparations on Growth Factor Content and In Vitro Cell Wound Healing

Blood-based preparations are used in clinical practice for the treatment of several eye disorders. The aim of this study is to analyze the effect of freeze-drying blood-based preparations on the levels of growth factors and wound healing behaviors in an in vitro model. Platelet-rich plasma (PRP) and serum (S) preparations from the same Cord Blood (CB) sample, prepared in both fresh frozen (FF) and freeze-dried (FD) forms (and then reconstituted), were analyzed for EGF and BDNF content (ELISA Quantikine kit). The human MIO-M1 glial cell line (Moorfield/Institute of Ophthalmology, London, UK) was incubated with FF and FD products and evaluated for cell migration with scratch-induced wounding (IncuCyte S3 Essen BioScience), proliferation with cyclin A2 and D1 gene expression, and activation with vimentin and GFAP gene expression. The FF and FD forms showed similar concentrations of EGF and BDNF in both the S and PRP preparations. The wound healing assay showed no significant difference between the FF and FD forms for both S and PRP. Additionally, cell migration, proliferation, and activation did not appear to change in the FD forms compared to the FF ones. Our study showed that reconstituted FD products maintained the growth factor concentrations and biological properties of FF products and could be used as a functional treatment option.     

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.